A Multi-Objective Optimization Method for Flexible Job Shop Scheduling Considering Cutting-Tool Degradation with Energy-Saving Measures

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and system, which not only limits potential but also leads to large deviation optimized result actual application. In practice, cutting-tool degradation during operation is inevitable, will lead increase in machining power resulting tool change disrupt rhythm production scheduling. Therefore, make energy consumption calculation more consistent with conditions reduce impact on manufacturing shop, this paper constructs an integrated model including flexible job problem (FJSP), prediction, life prediction strategy. First, exponential function formulated using cutting experiment data under certain express degradation. Utilizing function, reasonable schedule obtained. A hybrid strategy that combines turn-on/off schedules difference simulated while optimizing savings then proposed. Second, multi-objective was established makespan, total load, number times tools are turned on/off changed, workshop fast elitist genetic algorithm (NSGA-II) used solve model. Finally, combined cost evaluation indicator, practical FJSP example presented demonstrate proposed The accuracy than 93%. can further by 4.44% 2.44% basis saving 93.5% non-processing